Robust wall states in rapidly rotating Rayleigh–Bénard convection

Favier, Benjamin; Knobloch, Edgar

doi:10.1017/jfm.2020.310

Cited by 46 publications

(85 citation statements)

References 44 publications

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“…The maximum of the prograde flow occurs near the wall, and is located in the vicinity of the inner Stewartson layer, in good agreement with de Wit et al. (2020), Favier & Knobloch (2020) and Zhang et al. (2020).…”

Section: Resultssupporting

confidence: 88%

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Oscillatory thermal–inertial flows in liquid metal rotating convection

2021

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Section: Resultssupporting

confidence: 88%

“…(2020). They have been shown to be linked to nonlinear wall modes processes and Stewartson boundary layer like behaviours (Favier & Knobloch 2020; de Wit et al. 2020; Shishkina 2020).…”

Section: Resultsmentioning

confidence: 99%

Oscillatory thermal–inertial flows in liquid metal rotating convection

2021

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“…However, there is an important difference in domain composition: these studies typically use rectilinear domains with periodic boundary conditions in the horizontal directions rather than a cylinder. Sidewall modes [32][33][34][35] are then present in the laboratory experiments but not in DNS. The wall modes lead to increased Nu, with the effect more pronounced at lower Ra/Ra C [32].…”

Section: B Heat Transfermentioning

confidence: 96%

“…Recent simulations and experiments have focused on a property of the sidewall region: the persistent presence (beyond steady onset) of so-called wall modes in confined rotating convection [32][33][34][35]. These must be discussed in the analysis of our data.…”

Section: B Measurement Techniquesmentioning

confidence: 98%

“…In rotating convection, however, the shape of the temperature profile evolves as Ra/Ra C changes and with it the flow morphology, giving it diagnostic properties [17,37,38]. The aforementioned wall modes of confined convection [32][33][34][35] actually do not affect long-time averaged temperature values; their symmetrical structure (azimuthal distribution into a "colder" and a "warmer" half) and gradual azimuthal precession [32] make it so that the wall mode signature is averaged out in the sidewall thermistor time series. Hence the time-averaged sidewall gradient still nicely follows that of the bulk, independent of the presence of the wall mode (in the manner of the DNS comparison in Fig.…”

Section: Midheight Temperature Gradientmentioning

confidence: 99%

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Laboratory exploration of heat transfer regimes in rapidly rotating turbulent convection

et al. 2020

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Data‐driven identification of the spatiotemporal structure of turbulent flows by streaming dynamic mode decomposition

et al. 2022

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Streaming Dynamic Mode Decomposition (sDMD) is a low‐storage version of dynamic mode decomposition (DMD), a data‐driven method to extract spatiotemporal flow patterns. Streaming DMD avoids storing the entire data sequence in memory by approximating the dynamic modes through incremental updates with new available data. In this paper, we use sDMD to identify and extract dominant spatiotemporal structures of different turbulent flows, requiring the analysis of large datasets. First, the efficiency and accuracy of sDMD are compared to the classical DMD, using a publicly available test dataset that consists of velocity field snapshots obtained by direct numerical simulation of a wake flow behind a cylinder. Streaming DMD not only reliably reproduces the most important dynamical features of the flow; our calculations also highlight its advantage in terms of the required computational resources. We subsequently use sDMD to analyse three different turbulent flows that all show some degree of large‐scale coherence: rapidly rotating Rayleigh–Bénard convection, horizontal convection and the asymptotic suction boundary layer (ASBL). Structures of different frequencies and spatial extent can be clearly separated, and the prominent features of the dynamics are captured with just a few dynamic modes. In summary, we demonstrate that sDMD is a powerful tool for the identification of spatiotemporal structures in a wide range of turbulent flows.

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Robust wall states in rapidly rotating Rayleigh–Bénard convection

Cited by 46 publications

References 44 publications

Oscillatory thermal–inertial flows in liquid metal rotating convection

Oscillatory thermal–inertial flows in liquid metal rotating convection

Laboratory exploration of heat transfer regimes in rapidly rotating turbulent convection

Data‐driven identification of the spatiotemporal structure of turbulent flows by streaming dynamic mode decomposition

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